3D　object　reconstruction　from　arbitrary　view　intensity　images　is　a　challenging　but　meaningful　research　topic　in　computer　vision.　The　main　limitations　of　existing　approaches　are　that　they　lack　complete　and　efficient　prior　information　and　might　not　be　able　to　deal　with　serious　occlusion　or　partial　observation　of　3D　objects,　which　may　produce　incomplete　and　unreliable　reconstructions.　To　reconstruct　structure　and　recover　missing　or　unseen　parts　of　objects,　category　prior　and　intrinsic　geometry　relation　are　particularly　useful　and　necessary　during　the　3D　reconstruction　process.　In　this　paper,　we　propose　Category-and-Intrinsic-Geometry　Guided　Network　(CIGNet)　for　3D　coarse-to-fine　reconstruction　from　arbitrary　view　intensity　images　by　leveraging　category　prior　and　intrinsic　geometry　relation.　CIGNet　combines　a　category　prior　guided　reconstruction　module　with　an　intrinsic　geometry　relation　guided　refinement　module.　In　the　first　reconstruction　module,　we　leverage　semantic　class　context　by　adding　a　supervision　term　over　object　categories　to　output　coarse　reconstructed　results.　In　the　second　refinement　module,　we　model　the　coarse　3D　volumetric　data　as　2D　slices　and　consider　intrinsic　geometry　relations　between　them　to　design　graph　structures　of　coarse　3D　volumes　to　finish　the　graph　based　refinement.　CIGNet　can　accomplish　high-quality　3D　reconstruction　tasks　by　exploring　the　intra-category　characteristics　of　objects　as　well　as　the　intrinsic　geometry　relations　of　each　object,　both　of　which　serve　as　useful　complements　to　the　visual　information　of　images,　in　a　coarse-to-fine　fashion.　Extensive　quantitative　and　qualitative　experiments　on　a　synthetic　dataset　ShapeNet　and　real-world　datasets　Pix3D,　Statue　Model　Repository,　and　BlendedMVS　indicate　that　CIGNet　outperforms　several　state-of-the-art　methods　in　terms　of　accuracy　and　detail　recovery.